Transcending conventional snapshot polarimeter performance via neuromorphically adaptive filters

A channeled Stokes polarimeter that recovers polarimetric signatures across the scene from the modulation induced channels is preferrable for many polarimetric sensing applications. Conventional channeled systems that isolate the intended channels with low-pass filters are sensitive to channel crosstalk effects, and the filters have to be optimized based on the bandwidth profile of scene of interest before applying to each particular scenes to be measured. Here, we introduce a machine learning based channel filtering framework for channeled polarimeters. The machines are trained to predict anti-aliasing filters according to the distribution of the measured data adaptively. A conventional snapshot Stokes polarimeter is simulated to present our machine learning based channel filtering framework. Finally, we demonstrate the advantage of our filtering framework through the comparison of reconstructed polarimetric images with the conventional image reconstruction procedure. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

The study introduces a machine learning based channel filtering framework for channeled polarimeters, which trains machines to predict anti-aliasing filters adaptively. Through simulation experiments, the method demonstrates advantages in reconstructing polarimetric images.